基于自适应的配电网分布式电源优化配置研究

针对分布式电源接入配电网引起的电压越限和电能质量下降等问题,提出了一种具备自适应特性的分布式电源优化配置方法. 建立了光伏、风电两种典型分布式电源的数学模型,分析其功率输出特性. 构建了同时考虑发电成本、环境成本、有功网损折算成本三项指标的分布式电源优化配置模型. 针对多目标函数和多约束条件的优化配置模型,应用自适应粒子群算法求解,实现学习因子和惯性权重自适应调整以提高算法的寻优性能,由此得到分布式电源的最佳接入位置和容量. 最后,以IEEE33节点配电系统为例进行仿真验证. 结果表明,自适应粒子群算法与传统粒子群算法和混沌粒子群算法相比,求解得到的优化配置方案可达到更好的供电可靠性和经济性要求.

Research on Optimal Configuration of Distributed Power Supplyin Distribution Network Based on Adaptive Particle

Aiming at the problems of voltage over-limit and power quality degradation caused by distributed power supply distribution network,a distributed power supply optimization configuration method for distribution network with adaptive characteristics is proposed in this paper. The mathematical model of photovoltaic and wind power is established to analyze its power output characteristics. A distributed power optimization configuration model for distribution network is constructed considering the three factors of distributed power generation:cost,environmental cost and active network loss. For the optimal configuration model of multi-objective function and multi-constraint condition,the adaptive particle swarm optimization algorithm is applied to realize adaptive adjustment of learning factor and inertia weight to improve the optimization performance of the algorithm,thus obtaining the best Location and capacity of distributed power. Finally,the IEEE33 node power distribution system is taken as an example for simulation verification. The results show that the proposed adaptive particle swarm optimization algorithm can achieve better power supply reliability and economical requirements than the traditional particle swarm optimization algorithm and chaotic particle swarm optimization algorithm.